Run LGG and HGG survival by splicing cluster assignment and splicing burden

Purpose:

Runs survival analysis models using splicing cluster assignment and 1) single exon splicing burden index (SBI) or 2) KEGG Spliceosome GSVA scores as a predictor

Usage

Uses a wrapper function (survival_analysis) from utils folder.

Setup

Packages and functions

Load packages, set directory paths and call setup script

library(tidyverse)
library(survival)
library(ggpubr)
library(ggplot2)
library(patchwork)

root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))

data_dir <- file.path(root_dir, "data")
analysis_dir <- file.path(root_dir, "analyses", "survival")
input_dir <- file.path(analysis_dir, "results")
results_dir <- file.path(analysis_dir, "results")
plot_dir <- file.path(analysis_dir, "plots")

# If the input and results directories do not exist, create it
if (!dir.exists(results_dir)) {
  dir.create(results_dir, recursive = TRUE)
}

source(file.path(analysis_dir, "util", "survival_models.R"))

Set metadata and cluster assignment file paths

metadata_file <- file.path(input_dir, "splicing_indices_with_survival.tsv")

cluster_file <- file.path(root_dir, "analyses",
                          "sample-psi-clustering", "results",
                          "sample-cluster-metadata-top-5000-events-stranded.tsv")

kegg_scores_stranded_file <- file.path(root_dir, "analyses",
                          "sample-psi-clustering", "results",
                          "gsva_output_stranded.tsv")

Wrangle data Add cluster assignment and spliceosome gsva scores to metadata and define column lgg_group (LGG or non_LGG)

metadata <- read_tsv(metadata_file)

Rows: 1312 Columns: 23── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr  (9): Kids_First_Biospecimen_ID, Histology, Kids_First_Participant_ID, molecular_subtype, extent_of_tumor_resection, EFS_event_type, OS_status, EFS_status...
dbl (14): Total, AS_neg, AS_pos, AS_total, SI_A3SS, SI_A5SS, SI_RI, SI_SE, EFS_days, OS_days, age_at_diagnosis_days, age_at_diagnosis, OS_years, EFS_years
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

clusters <- read_tsv(cluster_file) %>%
  dplyr::rename(Kids_First_Biospecimen_ID = sample_id)

Rows: 752 Columns: 8── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (6): sample_id, plot_group, plot_group_hex, RNA_library, molecular_subtype, plot_group_n
dbl (2): cluster, group_n
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

gsva_scores <- read_tsv(kegg_scores_stranded_file) %>%
  dplyr::filter(geneset == "KEGG_SPLICEOSOME") %>%
  dplyr::rename(spliceosome_gsva_score = score)

Rows: 23312 Columns: 3── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (2): sample_id, geneset
dbl (1): score
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# how many clusters?
n_clust <- length(unique(clusters$cluster))

metadata <- metadata %>%
  right_join(clusters %>% dplyr::select(Kids_First_Biospecimen_ID,
                                       cluster)) %>%
  left_join(gsva_scores %>% dplyr::select(sample_id,
                                          spliceosome_gsva_score),
            by = c("Kids_First_Biospecimen_ID" = "sample_id")) %>% 
  dplyr::mutate(cluster = glue::glue("Cluster {cluster}")) %>%
  dplyr::mutate(cluster = fct_relevel(cluster,
                                               paste0("Cluster ", 1:n_clust))) %>%
  dplyr::mutate(lgg_group = case_when(
    Histology == "Low-grade glioma" ~ "LGG",
    TRUE ~ "non-LGG"
  )) %>%
  dplyr::mutate(SI_SE = SI_SE * 10)

Joining with `by = join_by(Kids_First_Biospecimen_ID)`

Generate coxph models including extent of tumor resection, lgg group, and cluster assignment and SBI as covariates

add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_days+SI_SE",
                               file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_SBI.RDS"),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_SBI.RDS")))

Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_errorbarh()`).Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).

forest_os

ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_SBI.pdf"),
       forest_os,
       width = 10, height = 6, units = "in",
       device = "pdf")

add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_days+SI_SE",
                               file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_SBI.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_SBI.RDS")))

Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_errorbarh()`).Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).

forest_efs

ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_SBI.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")

repeat analysis, replacing SBI with KEGG spliceosome gsva score

add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_days+spliceosome_gsva_score",
                               file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score.RDS"),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score.RDS")))

Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_errorbarh()`).Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).

forest_os

ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_spliceosome_score.pdf"),
       forest_os,
       width = 10, height = 6, units = "in",
       device = "pdf")

models <- c("spliceosome_gsva_score", "SI_SE")

for (each in models) {
  int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+cluster*", each, "+age_at_diagnosis_days"),
                                 file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS")),
                                 "multivariate",
                                 years_col = "EFS_years",
                                 status_col = "EFS_status")
  
  int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS"))))
  
  int_forest_efs
  
  ggsave(file.path(plot_dir, paste0("forest_int_EFS_resection_lgg_group_cluster_assignment_", each, ".pdf")),
         int_forest_efs,
         width = 10, height = 6, units = "in",
         device = "pdf")

  int_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+cluster*", each, "+age_at_diagnosis_days"),
                                 file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS")),
                                 "multivariate",
                                 years_col = "OS_years",
                                 status_col = "OS_status")
  
  int_forest_os <- plotForest(readRDS(file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS"))))
  
  int_forest_os
  
  ggsave(file.path(plot_dir, paste0("forest_int_OS_resection_lgg_group_cluster_assignment_", each, ".pdf")),
         int_forest_os,
         width = 10, height = 6, units = "in",
         device = "pdf")
  
}

Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_errorbarh()`).Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).

add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_days+spliceosome_gsva_score",
                               file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score.RDS")))

Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_errorbarh()`).Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).

forest_efs

ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_spliceosome_score.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")

Subset metadata for LGG, and only include clusters with >= 10 samples

lgg <- metadata %>%
  dplyr::filter(Histology == "Low-grade glioma") %>%
  dplyr::mutate(cluster = factor(cluster)) %>%
  dplyr::mutate(mol_sub_group = fct_relevel(mol_sub_group, c("Wildtype", "BRAF V600E", "BRAF fusion",
                                                                "Other alteration", "SEGA"
                                                                )))

retain_clusters_lgg <- lgg %>%
  dplyr::count(cluster) %>%
  filter(n >= 10) %>%
  pull(cluster)

lgg <- lgg %>%
  filter(cluster %in% retain_clusters_lgg) %>%
    dplyr::mutate(cluster = factor(cluster))

Generate coxph models including covariates extent_of_tumor_resection, mol_sub_group, cluster, and SI_SE and plot

# identify LGG clusters
lgg_clusters <- metadata %>%
  filter(lgg_group == "LGG") %>%
  mutate(cluster = as.integer(gsub("cluster", "", cluster))) %>%
  pull(cluster) %>%
  sort() %>%
  unique()

Warning: There was 1 warning in `mutate()`.
ℹ In argument: `cluster = as.integer(gsub("cluster", "", cluster))`.
Caused by warning:
! NAs introduced by coercion

add_model_lgg_efs <- fit_save_model(lgg[!lgg$cluster %in% lgg_clusters & !lgg$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+mol_sub_group+cluster+age_at_diagnosis_days+SI_SE",
                               file.path(results_dir, "cox_lgg_EFS_additive_terms_resection_subtype_cluster_SBI.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

Warning: Loglik converged before variable  6,8 ; coefficient may be infinite. 

forest_lgg_efs <- plotForest(readRDS(file.path(results_dir, "cox_lgg_EFS_additive_terms_resection_subtype_cluster_SBI.RDS")))

Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_errorbarh()`).Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).

forest_lgg_efs

ggsave(file.path(plot_dir, "forest_add_EFS_LGG_resection_subtype_cluster_assignment_SBI.pdf"),
       forest_lgg_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")

repeat analysis replacing SI_SE with spliceosome_gsva_score

add_model_lgg_efs <- fit_save_model(lgg[!lgg$cluster %in% lgg_clusters & !lgg$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+mol_sub_group+cluster+age_at_diagnosis_days+spliceosome_gsva_score",
                               file.path(results_dir, "cox_lgg_EFS_additive_terms_resection_subtype_cluster_spliceosome_score.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

Warning: Loglik converged before variable  6,8 ; coefficient may be infinite. 

forest_lgg_efs <- plotForest(readRDS(file.path(results_dir, "cox_lgg_EFS_additive_terms_resection_subtype_cluster_spliceosome_score.RDS")))

Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_errorbarh()`).Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).

forest_lgg_efs

ggsave(file.path(plot_dir, "forest_add_EFS_LGG_resection_subtype_cluster_assignment_spliceosome_score.pdf"),
       forest_lgg_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")

Subset metadata for HGG and retain cluster with n >= 10

hgg <- metadata %>%
  dplyr::filter(Histology %in% c("Other high-grade glioma", "DIPG or DMG")) %>%
  dplyr::mutate(cluster = factor(cluster)) %>%
  dplyr::mutate(mol_sub_group = fct_relevel(mol_sub_group, c("HGG, H3 wildtype", "HGG, H3 wildtype, TP53",
                                                             "DMG, H3 K28", "DMG, H3 K28, TP53",
                                                                "DHG, H3 G35", "DHG, H3 G35, TP53",
                                                                "HGG, IDH, TP53", "HGG, PXA", "HGG, PXA, TP53", 
                                                                "IHG, ALK-altered", "IHG, NTRK-altered",
                                                                "IHG, ROS1-altered"
                                                                ))) %>%
  dplyr::filter(!is.na(OS_days) | !is.na(EFS_days))

Warning: There was 1 warning in `dplyr::mutate()`.
ℹ In argument: `mol_sub_group = fct_relevel(...)`.
Caused by warning:
! 1 unknown level in `f`: HGG, PXA, TP53

retain_clusters_hgg <- hgg %>%
  dplyr::count(cluster) %>%
  filter(n >= 10) %>%
  pull(cluster)

hgg <- hgg %>%
  filter(cluster %in% retain_clusters_hgg) %>%
  dplyr::mutate(cluster = factor(cluster)) %>%
    dplyr::mutate(SI_group = case_when(
      SI_SE > summary(SI_SE)["3rd Qu."] ~ "High SBI",
      SI_SE < summary(SI_SE)["1st Qu."] ~ "Low SBI",
      TRUE ~ NA_character_
    )) %>%
  dplyr::mutate(spliceosome_group = case_when(
      spliceosome_gsva_score > summary(spliceosome_gsva_score)["3rd Qu."] ~ "Splice GSVA 4th Q",
      spliceosome_gsva_score > summary(spliceosome_gsva_score)["Median"] ~ "Splice GSVA 3rd Q",
      spliceosome_gsva_score > summary(spliceosome_gsva_score)["1st Qu."] ~ "Splice GSVA 2nd Q",
      TRUE ~ "Splice GSVA 1st Q"
    )) %>%
  dplyr::mutate(SI_group = fct_relevel(SI_group,
                                                 c("High SBI", "Low SBI"))) %>%
  dplyr::mutate(spliceosome_group = fct_relevel(spliceosome_group,
                                                 c("Splice GSVA 1st Q", 
                                                   "Splice GSVA 2nd Q", 
                                                   "Splice GSVA 3rd Q",
                                                   "Splice GSVA 4th Q")))

Generate HGG KM models with spliceosome_group as covariate

# Generate kaplan meier survival models for OS and EFS, and save outputs
hgg_kap_os <- survival_analysis(
  metadata  = hgg %>% dplyr::filter(!is.na(spliceosome_group)),
  ind_var = "spliceosome_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "OS_days",
  status_col = "OS_status"
)

Testing model: survival::Surv(OS_days, OS_status) ~ spliceosome_group with kap.meier

readr::write_rds(hgg_kap_os,
                 file.path(results_dir, "logrank_hgg_OS_splice_group.RDS"))

hgg_kap_efs <- survival_analysis(
  metadata  = hgg %>% dplyr::filter(!is.na(spliceosome_group)),
  ind_var = "spliceosome_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "EFS_days",
  status_col = "EFS_status"
)

Testing model: survival::Surv(EFS_days, EFS_status) ~ spliceosome_group with kap.meier

readr::write_rds(hgg_kap_efs,
                 file.path(results_dir, "logrank_hgg_EFS_splice_group.RDS"))

Generate KM plots

km_hgg_os_plot <- plotKM(model = hgg_kap_os,
                    variable = "spliceosome_group",
                    combined = F, 
                    title = "HGG, overall survival",
                    p_pos = "topright")

Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.

ggsave(file.path(plot_dir, "km_hgg_OS_spliceosome_score.pdf"),
       km_hgg_os_plot,
       width = 9, height = 5, units = "in",
       device = "pdf")

km_hgg_efs_plot <- plotKM(model = hgg_kap_efs,
                    variable = "spliceosome_group",
                    combined = F, 
                    title = "HGG, event-free survival",
                    p_pos = "topright")

ggsave(file.path(plot_dir, "km_hgg_EFS_spliceosome_score.pdf"), 
       km_hgg_efs_plot,
       width = 9, height = 5, units = "in",
       device = "pdf")

Generate coxph models for HGG including covariates mol_sub_group cluster, and SI_SE, and plot

add_model_hgg_os <- fit_save_model(hgg,
                              terms = "mol_sub_group+cluster+age_at_diagnosis_days+SI_SE",
                               file.path(results_dir, "cox_hgg_OS_additive_terms_subtype_cluster_SBI.RDS"),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

Warning: Loglik converged before variable  9 ; coefficient may be infinite. 

forest_hgg_os <- plotForest(readRDS(file.path(results_dir, "cox_hgg_OS_additive_terms_subtype_cluster_SBI.RDS")))

Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_errorbarh()`).Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).

forest_hgg_os

ggsave(file.path(plot_dir, "forest_add_OS_HGG_subtype_cluster_assignment_SBI.pdf"),
       forest_hgg_os,
       width = 9, height = 5, units = "in",
       device = "pdf")

add_model_hgg_efs <- fit_save_model(hgg,
                              terms = "mol_sub_group+cluster+age_at_diagnosis_days+SI_SE",
                               file.path(results_dir, "cox_hgg_EFS_additive_terms_subtype_cluster_SBI.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_hgg_efs <- plotForest(readRDS(file.path(results_dir, "cox_hgg_EFS_additive_terms_subtype_cluster_SBI.RDS")))

Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_errorbarh()`).Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).

ggsave(file.path(plot_dir, "forest_add_EFS_HGG_subtype_cluster_assignment_SBI.pdf"),
       forest_hgg_efs,
       width = 9, height = 5, units = "in",
       device = "pdf")

Repeat analysis replacing SI_SE with spliceosome_gsva_score

add_model_hgg_os <- fit_save_model(hgg,
                              terms = "mol_sub_group+cluster+age_at_diagnosis_days+spliceosome_gsva_score",
                               file.path(results_dir, "cox_hgg_OS_additive_terms_subtype_cluster_spliceosome_score.RDS"),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

Warning: Loglik converged before variable  9 ; coefficient may be infinite. 

forest_hgg_os <- plotForest(readRDS(file.path(results_dir, "cox_hgg_OS_additive_terms_subtype_cluster_spliceosome_score.RDS")))

Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_errorbarh()`).Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).

forest_hgg_os

ggsave(file.path(plot_dir, "forest_add_OS_HGG_subtype_cluster_assignment_spliceosome_score.pdf"),
       forest_hgg_os,
       width = 9, height = 5, units = "in",
       device = "pdf")

add_model_hgg_efs <- fit_save_model(hgg,
                              terms = "mol_sub_group+cluster+age_at_diagnosis_days+spliceosome_gsva_score",
                               file.path(results_dir, "cox_hgg_EFS_additive_terms_subtype_cluster_spliceosome_score.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_hgg_efs <- plotForest(readRDS(file.path(results_dir, "cox_hgg_EFS_additive_terms_subtype_cluster_spliceosome_score.RDS")))

Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_errorbarh()`).Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).

ggsave(file.path(plot_dir, "forest_add_EFS_HGG_subtype_cluster_assignment_spliceosome_score.pdf"),
       forest_hgg_efs,
       width = 9, height = 5, units = "in",
       device = "pdf")

Filter for cluster 6

cluster6_df <- metadata %>%
  dplyr::filter(cluster == "Cluster 6",
                !is.na(EFS_days)) %>%
  dplyr::mutate(SI_group = case_when(
      SI_SE > summary(SI_SE)["3rd Qu."] ~ "High SBI",
      SI_SE < summary(SI_SE)["1st Qu."] ~ "Low SBI",
      TRUE ~ NA_character_
    )) %>%
  dplyr::mutate(spliceosome_group = case_when(
      spliceosome_gsva_score > summary(spliceosome_gsva_score)["3rd Qu."] ~ "Splice GSVA 4th Q",
      spliceosome_gsva_score > summary(spliceosome_gsva_score)["Median"] ~ "Splice GSVA 3rd Q",
      spliceosome_gsva_score > summary(spliceosome_gsva_score)["1st Qu."] ~ "Splice GSVA 2nd Q",
      TRUE ~ "Splice GSVA 1st Q"
    )) %>%
  dplyr::mutate(SI_group = fct_relevel(SI_group,
                                                 c("High SBI", "Low SBI"))) %>%
  dplyr::mutate(spliceosome_group = fct_relevel(spliceosome_group,
                                                 c("Splice GSVA 1st Q", 
                                                   "Splice GSVA 2nd Q", 
                                                   "Splice GSVA 3rd Q",
                                                   "Splice GSVA 4th Q")))

Generate KM models with SI_group as covariate

# Generate kaplan meier survival models for OS and EFS, and save outputs
c6_si_kap_os <- survival_analysis(
  metadata  = cluster6_df %>% dplyr::filter(!is.na(SI_group)),
  ind_var = "SI_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "OS_days",
  status_col = "OS_status"
)

Testing model: survival::Surv(OS_days, OS_status) ~ SI_group with kap.meier

readr::write_rds(c6_si_kap_os,
                 file.path(results_dir, "logrank_cluster6_OS_SBI.RDS"))

c6_si_kap_efs <- survival_analysis(
  metadata  = cluster6_df %>% dplyr::filter(!is.na(SI_group)),
  ind_var = "SI_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "EFS_days",
  status_col = "EFS_status"
)

Testing model: survival::Surv(EFS_days, EFS_status) ~ SI_group with kap.meier

readr::write_rds(c6_si_kap_efs,
                 file.path(results_dir, "logrank_cluster6_EFS_SBI.RDS"))

Generate Cluster 6 KM SI_group plots

km_c6_si_os_plot <- plotKM(model = c6_si_kap_os,
                    variable = "SI_group",
                    combined = F, 
                    title = "Cluster 6, overall survival",
                    p_pos = "topright")

Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.

ggsave(file.path(plot_dir, "km_cluster6_OS_sbi_group.pdf"),
       km_c6_si_os_plot,
       width = 8, height = 5, units = "in",
       device = "pdf")

km_c6_si_efs_plot <- plotKM(model = c6_si_kap_efs,
                    variable = "SI_group",
                    combined = F, 
                    title = "Cluster 6, event-free survival",
                    p_pos = "topright")

ggsave(file.path(plot_dir, "km_cluster6_EFS_sbi_group.pdf"), 
       km_c6_si_efs_plot,
       width = 8, height = 5, units = "in",
       device = "pdf")

Generate KM models with spliceosome_group as covariate

# Generate kaplan meier survival models for OS and EFS, and save outputs
c6_splice_kap_os <- survival_analysis(
  metadata  = cluster6_df %>% 
    dplyr::filter(spliceosome_group %in% c("Splice GSVA 4th Q", "Splice GSVA 1st Q")) %>%
    dplyr::mutate(spliceosome_group = factor(spliceosome_group,
                                                  levels = c("Splice GSVA 1st Q", "Splice GSVA 4th Q"))),
  ind_var = "spliceosome_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "OS_days",
  status_col = "OS_status"
)

Testing model: survival::Surv(OS_days, OS_status) ~ spliceosome_group with kap.meier

readr::write_rds(c6_splice_kap_os,
                 file.path(results_dir, "logrank_cluster6_OS_splice_group.RDS"))

c6_splice_kap_efs <- survival_analysis(
  metadata  = cluster6_df %>% 
    dplyr::filter(spliceosome_group %in% c("Splice GSVA 4th Q", "Splice GSVA 1st Q")) %>%
    dplyr::mutate(spliceosome_group = factor(spliceosome_group,
                                                  levels = c("Splice GSVA 1st Q", "Splice GSVA 4th Q"))),
  ind_var = "spliceosome_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "EFS_days",
  status_col = "EFS_status"
)

Testing model: survival::Surv(EFS_days, EFS_status) ~ spliceosome_group with kap.meier

readr::write_rds(c6_splice_kap_efs,
                 file.path(results_dir, "logrank_cluster6_EFS_splice_group.RDS"))

Generate Cluster 6 KM spliceosome_group plots

km_c6_splice_os_plot <- plotKM(model = c6_splice_kap_os,
                    variable = "spliceosome_group",
                    combined = F, 
                    title = "Cluster 6, overall survival",
                    p_pos = "topright")

Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.

ggsave(file.path(plot_dir, "km_cluster6_OS_splice_group.pdf"),
       km_c6_splice_os_plot,
       width = 9, height = 5, units = "in",
       device = "pdf")

km_c6_splice_efs_plot <- plotKM(model = c6_splice_kap_efs,
                    variable = "spliceosome_group",
                    combined = F, 
                    title = "Cluster 6, event-free survival",
                    p_pos = "topright")

ggsave(file.path(plot_dir, "km_cluster6_EFS_splice_group.pdf"), 
       km_c6_splice_efs_plot,
       width = 9, height = 5, units = "in",
       device = "pdf")

Assess EFS and OS by SBI or spliceosome GSVA score in multivariate models and generate forest plots

add_model_c6_efs <- fit_save_model(cluster6_df %>% 
                                      dplyr::filter(extent_of_tumor_resection != "Unavailable",
                                                    spliceosome_group %in% c("Splice GSVA 4th Q", "Splice GSVA 1st Q")) %>%
                                      dplyr::mutate(Histology = fct_relevel(Histology,
                                                                            c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                                                              "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                                                              "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_days+Histology+spliceosome_group",
                               file.path(results_dir, "cox_hgg_EFS_additive_terms_subtype_cluster_spliceosome_score.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

Warning: There was 1 warning in `dplyr::mutate()`.
ℹ In argument: `Histology = fct_relevel(...)`.
Caused by warning:
! 2 unknown levels in `f`: Ependymoma and Low-grade glioma

forest_c6_spliceosome_efs <- plotForest(readRDS(file.path(results_dir, "cox_hgg_EFS_additive_terms_subtype_cluster_spliceosome_score.RDS")))

Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_errorbarh()`).Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).

ggsave(file.path(plot_dir, "forest_add_EFS_cluster6_histology_resection_spliceosome_group.pdf"),
       forest_c6_spliceosome_efs,
       width = 9, height = 4, units = "in",
       device = "pdf")

add_model_c6_os <- fit_save_model(cluster6_df %>% 
                                    dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")) %>%
                                    dplyr::rename("SBI_SE" = SI_SE) %>%
                                    dplyr::mutate(Histology = fct_relevel(Histology,
                                                                            c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                                                              "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                                                              "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_days+Histology+SBI_SE",
                               file.path(results_dir, "cox_hgg_OS_additive_terms_subtype_cluster_si_group.RDS"),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

Warning: Loglik converged before variable  9 ; coefficient may be infinite. 

forest_c6_si_os <- plotForest(readRDS(file.path(results_dir, "cox_hgg_OS_additive_terms_subtype_cluster_si_group.RDS")))

Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_errorbarh()`).Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).

ggsave(file.path(plot_dir, "forest_add_OS_cluster6_histology_resection_si.pdf"),
       forest_c6_si_os,
       width = 9, height = 4, units = "in",
       device = "pdf")

Print session info

sessionInfo()

R version 4.4.2 (2024-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.5

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] grid      stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gtools_3.9.5                survminer_0.5.0             patchwork_1.3.0             ggpubr_0.6.0                survival_3.7-0             
 [6] ggthemes_5.1.0              rprojroot_2.0.4             ggrepel_0.9.6               DESeq2_1.46.0               SummarizedExperiment_1.36.0
[11] Biobase_2.66.0              MatrixGenerics_1.18.1       matrixStats_1.5.0           GenomicRanges_1.58.0        GenomeInfoDb_1.42.3        
[16] IRanges_2.40.1              S4Vectors_0.44.0            BiocGenerics_0.52.0         lubridate_1.9.4             forcats_1.0.0              
[21] stringr_1.5.1               dplyr_1.1.4                 purrr_1.0.2                 readr_2.1.5                 tidyr_1.3.1                
[26] tibble_3.2.1                ggplot2_3.5.1               tidyverse_2.0.0            

loaded via a namespace (and not attached):
 [1] gridExtra_2.3           rlang_1.1.5             magrittr_2.0.3          compiler_4.4.2          systemfonts_1.2.1       vctrs_0.6.5            
 [7] pkgconfig_2.0.3         crayon_1.5.3            fastmap_1.2.0           backports_1.5.0         XVector_0.46.0          labeling_0.4.3         
[13] KMsurv_0.1-5            utf8_1.2.4              rmarkdown_2.29          markdown_1.13           tzdb_0.4.0              UCSC.utils_1.2.0       
[19] ragg_1.3.3              bit_4.5.0.1             xfun_0.50               cachem_1.1.0            zlibbioc_1.52.0         jsonlite_1.9.1         
[25] DelayedArray_0.32.0     BiocParallel_1.40.0     broom_1.0.7             parallel_4.4.2          R6_2.6.1                bslib_0.9.0            
[31] stringi_1.8.4           car_3.1-3               jquerylib_0.1.4         Rcpp_1.0.14             knitr_1.49              zoo_1.8-12             
[37] Matrix_1.7-1            splines_4.4.2           timechange_0.3.0        tidyselect_1.2.1        rstudioapi_0.17.1       abind_1.4-8            
[43] yaml_2.3.10             ggtext_0.1.2            codetools_0.2-20        lattice_0.22-6          withr_3.0.2             evaluate_1.0.3         
[49] xml2_1.3.6              survMisc_0.5.6          pillar_1.10.1           colorblindr_0.1.0       carData_3.0-5           rsconnect_1.3.4        
[55] generics_0.1.3          vroom_1.6.5             hms_1.1.3               commonmark_1.9.5        munsell_0.5.1           scales_1.3.0           
[61] xtable_1.8-4            glue_1.8.0              tools_4.4.2             data.table_1.16.4       locfit_1.5-9.12         ggsignif_0.6.4         
[67] cowplot_1.1.3.9000      colorspace_2.1-1        GenomeInfoDbData_1.2.13 Formula_1.2-5           cli_3.6.4               km.ci_0.5-6            
[73] textshaping_1.0.0       S4Arrays_1.6.0          gtable_0.3.6            rstatix_0.7.2           sass_0.4.9              digest_0.6.37          
[79] SparseArray_1.6.2       farver_2.1.2            htmltools_0.5.8.1       lifecycle_1.0.4         httr_1.4.7              gridtext_0.1.5         
[85] bit64_4.6.0-1